Abstract EC bit is an expandable and contractable bit a new concept of drilling the boreholes of different radii at different depths and in zone of our choice without changing the size of bit and without tripping operations. During conventional drilling boreholes of different radii at respective depths are being drilled. In order to drill boreholes of different radii bits of different sizes are manufactured which consumes tremendous amount of material, time and finally the cost. In order to face this challenge here a tool has been proposed which uses single comprehensive bit size to drill complete well. The mechanism of this tool is based on the concept of rack and pinion system. The rotation of gears (pinions) and movement of the racks will help us to drill the borehole of radii of our choice. With the help of this tool the important advantage is that the drillstring is not changed every time, only one size of drillstring is being used to drill complete well. This revolutionary idea of manufacturing ONLY ONE BIT SIZE will cost the drilling contractor a tremendous reduction in costs of bits. The other advantages include enhance production, reduction in skin damage and control the problem of sand production. Introduction: The width of a hole at every depth especially in the payzone is limited using the conventional drilling methods. This is because hole size is limited due to factors like cost of drilling a wide hole, rate of penetration, power requirements, size of BOP required, larger rigs required to handle more power and more weight, hence dramatically escalating the cost of the project. Having large borehole at every depth or in the payzone is beneficial because it would: Enhance production Control the problem of sand production Accommodate a lot of downhole equipment. Expandable and Contractable (EC) bit allows us to enlarge the borehole radius up to the order of a few feet selectively in zones we choose. This is made possible by the EC bit, which uses rack and pinion mechanism (Fig-1) with its unique clockwise and anticlockwise rotation to drill a superwide borehole.

Abstract Presented is a new methodology for selecting rotary drilling bits in an oil or gas well. Currently, bits are selected based on the performance of similar bits at offset wells. Parameters affecting a bit performance have a complex pattern. The relationship between formation properties, drilling fluid characteristics, bit design, and operational parameters in these patterns are not easily understood. For a given field, studied were variables such as bit size, weight on bit, rotary speed, pump rate, drilled interval, and bit type. A three-layer artificial neural network was designed and trained with field data. This method incorporates computational intelligence to define the relationship between the variables. Further, it can be used to estimate other drilling parameters. The results indicate that the back propagation achitecture with two hidden slabs is the most effective neural network design for predicting the optimum bit type. With the given data sets, this new model successfully predicted the bit types for several fields. For different data sets used in this study, the correlation coefficients for the predicted and field used bit types ranged between 0.857 and 0.975. Introduction Drilling engineers deal with many challenges before and during drilling a new well even in a known area. There are many parameters related to hardware and daily operations that are planned and also modified as the drilling progress. Bit selection is one of the important parameters for planning and designing a new oil or gas well. The selection of a proper bit is a difficult task since the factors affecting the bit performance are complex relationships between formation properties, bit hardware design, and operational parameters. Formation properties such as hardness and drillability are based on the interval drilled and they can not be changed. Additionally, they are not measured prior to drilling and they have to be estimated from geophysical surveys or from offset well records. Several investigators conducted research to estimate the bit behavior based on operational parameters and data recorded at offset wells. 1–10 Different models were developed by these researchers with assumptions limiting the applicability of their model. In general, the data from offset wells are used by the engineer to select a proper bit. The bit type with the highest rate of penetration or minimum cost per foot are the two commonly used criterias for selecting the bit for the next interval. Additional factors such as hydraulics, formation hardness, bit design, and operational parameters are also considered in the selection process. Due to the number of variables considered, the selection process is a trial and error procedure. In many cases, this approach can ignore some of the important parameters affecting the bit performance and can not guarantee the selection of the optimum bit type. When sufficient data exists, the use of neural networks are demonstrated to identify complex relationships. 11–13 The increased availability of down hole measuring tools resulted in data sets with many recorded variables related to the drilling process. As a result, different neural networks can be designed for a region or a field to predict unknown parameters. Approach In this study, a new methodology is introduced to select rotary drilling bits. This approach uses a three-layer feed forward neural network to select bit types. Several neural network models were used to determine the complex relationship between formation and bit properties together with operating parameters.